{
"cells": [
{
"cell_type": "markdown",
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},
"source": [
"#### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datetime import timedelta\n",
"import json\n",
"import pprint\n",
"from copy import deepcopy\n",
"\n",
"import cufflinks\n",
"cufflinks.go_offline()\n",
"import numpy as np\n",
"from numpy.polynomial import Polynomial\n",
"import pandas as pd\n",
"from tqdm.notebook import tqdm\n",
"\n",
"from pyrecoy.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n",
"from pyrecoy.colors import *\n",
"from pyrecoy.converters import *\n",
"from pyrecoy.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n",
"from pyrecoy.framework import TimeFramework\n",
"from pyrecoy.casestudy import CaseStudy\n",
"from pyrecoy.plotting import ebitda_bar_chart, npv_bar_chart\n",
"from pyrecoy.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n",
"from pyrecoy.sensitivity import SensitivityAnalysis\n",
"from pyrecoy.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
"from pyrecoy.forecasts import Mipf\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"#### Development backlog"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"* aFRR (can improve the optimisation case)\n",
"* Report\n",
"\n",
"--\n",
"* Create strategy on imbalance POS (buy 100% day-ahead, and respond to high prices)\n",
"* Graphs\n",
" * EBITDA vs. baseline (earnings vs baseline)\n",
"* Show COP curves in different cases, just for illustration\n",
"* Energy report --> Check + add gas\n",
"* Fix comparison reports\n",
"* Model verification"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"#### Meeting Notes"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"##### Meeting 25-11-2020"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"* aFRR can help optimisation case\n",
"* SDE++ should be included\n",
"* Tsource sensitivity really gives interesting insights\n",
"* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
"* AP TNO: Update/verify COP curve\n",
"* AP TNO: Update CAPEX\n",
"* AP Mark: \n",
" * Create graphs on COP curve with different Tsource, Tsink\n",
" * Generate table and output in .csv (send it to Andrew)\n",
"* Investigate opportunity to lower the COP and negative electricity prices\n",
" * Technically feasible, but not really needed/possible to do it in this project\n",
"* Could be interesting to run this model on a usecase with higher delta T\n",
"* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"# ENCORE : Heatpump Optimisation Framework\n",
"\n",
"***\n",
"© Mark Kremer \n",
"July 2020\n",
"##### Cases\n",
"In this model, 3 cases are compared:\n",
"* **Baseline** : All heat is supplied by steam turbine\n",
"* **Heatpump case** : All heat is supplied by heatpump\n",
"* **Hybrid case** : Steam turbine and heatpump run in hybrid mode, and are optimised on costs in real-time"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"#### Loading config"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [],
"source": [
"class Config():\n",
" start = '2019-01-01'\n",
" end = '2019-12-31'\n",
" \n",
" hp_vdg_e_power = 23.3 # MW\n",
" hp_ndg_e_power = 7.7 # MW\n",
" hp_min_load = 0\n",
" hp_lifetime = 25\n",
" hp_capex = 200_000 # EUR/MWth\n",
" hp_opex = 0.01 # in % of CAPEX\n",
" hp_devex = 0.005 # in % of CAPEX\n",
" \n",
" gb_power = 35 # MW\n",
" gb_efficiency = 0.9\n",
" \n",
" storage_power = 25\n",
" storage_volume = 1000\n",
" storage_cap_per_volume = 50 * 1e-3\n",
" storage_lifetime = 25\n",
" storage_temperature = 95\n",
" storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n",
" storage_capex_per_MW = 7_000\n",
" storage_capex_per_MWh = 5_000\n",
" storage_opex_perc_of_capex = 0.02\n",
" # storage_initial_level = 5\n",
" threshold = 20\n",
" \n",
" tax_bracket_g = 4 \n",
" tax_bracket_e = 4\n",
" \n",
" include_transport_costs = False\n",
" grid_operator = 'Liander'\n",
" connection_type = 'TS/MS'\n",
" \n",
" discount_rate = 0.1\n",
" project_duration = 12\n",
"\n",
" forecast = 'ForeNeg'\n",
" gas_price_multiplier = 1\n",
" e_price_multiplier = 1\n",
" e_price_volatility_multiplier = 1\n",
" co2_price_multiplier = 1\n",
" tsource_delta = 0\n",
" tsink_delta = 0\n",
" energy_tax_multiplier = 1\n",
" \n",
" # Review the SDE implementation\n",
" sde_base_amount = 81\n",
" longterm_gas_price = 24.00\n",
" longterm_co2_price = 37.90\n",
" sde_switch_price_correction = 40\n",
" \n",
" day_ahead_buying_perc = 0.3\n",
" \n",
" afrr_capacity_fee = 25_000\n",
"\n",
"c = Config()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [],
"source": [
"class Store():\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"## Model set-up"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": [
"exclude"
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},
"outputs": [],
"source": [
"def load_demand_data(c, s):\n",
" demand = pd.read_csv('data/smurfit_demand_preprocessed.csv', delimiter=';', decimal=',')\n",
" dt_index = pd.date_range(\n",
" start=s.time_fw.start,\n",
" end=s.time_fw.start + timedelta(days=365), freq='1T',\n",
" closed='left',\n",
" tz='Europe/Amsterdam')\n",
" demand.index = dt_index\n",
" demand['Total demand'] = demand['MW (VDG)'] + demand['MW (NDG)']\n",
" demand = demand[c.start:c.end]\n",
" return demand"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"s = Store()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\shahla.huseynova\\documents\\asset_studies_recoy\\asset-case-studies\\pyrecoy\\pyrecoy\\pyrecoy\\framework.py:40: UserWarning:\n",
"\n",
"The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: Entries missing from dataset 'imbalanceRT_nl.csv'.\n",
"Issue solved: Dataset was reindexed automatically.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_42308\\332434720.py:3: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n"
]
}
],
"source": [
"def setup_model(c):\n",
" s = Store()\n",
" \n",
" s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
" s.mipf = Mipf(start=s.time_fw.start, end=s.time_fw.end, tidy=True, include_nextQ=True,\n",
" folder_path = r\"C:\\Users\\Shahla Huseynova\\Recoy\\Recoy - Documents\\03 - Libraries\\12 - Data Management\\Forecast Data\"\n",
" ).data\n",
" \n",
" s.demand = load_demand_data(c, s)\n",
" return s\n",
"\n",
"s = setup_model(c)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": true
},
"data": [
{
"line": {
"color": "rgba(255, 153, 51, 1.0)",
"dash": "solid",
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# s.demand.iplot()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Tsource (VDG) | \n",
" Tsink (VDG) | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 63.658798 | \n",
" 138.637674 | \n",
" 17.636712 | \n",
" 62.176305 | \n",
" 145.351645 | \n",
" 5.823824 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 6.422773 | \n",
" 14.328580 | \n",
" 5.123252 | \n",
" 10.252555 | \n",
" 14.896056 | \n",
" 1.691697 | \n",
" 6.814125 | \n",
"
\n",
" \n",
" | min | \n",
" 25.310000 | \n",
" 82.480000 | \n",
" 0.000000 | \n",
" 13.540000 | \n",
" 106.330000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 64.892000 | \n",
" 126.074000 | \n",
" 17.014000 | \n",
" 63.516000 | \n",
" 131.551500 | \n",
" 5.612000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 64.991000 | \n",
" 142.450000 | \n",
" 19.892000 | \n",
" 64.313000 | \n",
" 148.490000 | \n",
" 6.540000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 65.076000 | \n",
" 151.730000 | \n",
" 20.819000 | \n",
" 65.546000 | \n",
" 158.100000 | \n",
" 6.880000 | \n",
" 27.690000 | \n",
"
\n",
" \n",
" | max | \n",
" 70.070000 | \n",
" 166.020000 | \n",
" 23.250000 | \n",
" 73.470000 | \n",
" 172.050000 | \n",
" 7.650000 | \n",
" 30.900000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" CO2 prices (€/MWh) | \n",
" Tsource (VDG) | \n",
" Tsink (VDG) | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-01-01 00:00:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 46.83 | \n",
" 52.70 | \n",
" NaN | \n",
" NaN | \n",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \n",
" 64.990 | \n",
" 143.360 | \n",
" 0.0 | \n",
" 20.510 | \n",
" 147.620 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:01:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 32.12 | \n",
" 54.45 | \n",
" NaN | \n",
" NaN | \n",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \n",
" 64.985 | \n",
" 143.089 | \n",
" 0.0 | \n",
" 20.387 | \n",
" 147.642 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:02:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 30.97 | \n",
" 48.64 | \n",
" NaN | \n",
" NaN | \n",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \n",
" 64.980 | \n",
" 142.818 | \n",
" 0.0 | \n",
" 20.264 | \n",
" 147.664 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:03:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 51.13 | \n",
" 48.35 | \n",
" 57.71 | \n",
" 73.05 | \n",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \n",
" 64.975 | \n",
" 142.547 | \n",
" 0.0 | \n",
" 20.141 | \n",
" 147.686 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:04:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 48.04 | \n",
" 52.01 | \n",
" 63.60 | \n",
" 79.07 | \n",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \n",
" 64.970 | \n",
" 142.276 | \n",
" 0.0 | \n",
" 20.018 | \n",
" 147.708 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" ... | \n",
" from_storage_MW | \n",
" storage_level_MWh | \n",
" hp_output_MWh | \n",
" hp_input_MWh | \n",
" to_storage_ndg_MWh | \n",
" from_storage_ndg_MWh | \n",
" to_storage_vdg_MWh | \n",
" from_storage_vdg_MWh | \n",
" to_storage_MWh | \n",
" from_storage_MWh | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-11-10 22:49:00+01:00 | \n",
" 44.08 | \n",
" 26.46 | \n",
" 26.46 | \n",
" -1.0 | \n",
" 14.59 | \n",
" 17.15 | \n",
" 30.43 | \n",
" 30.43 | \n",
" 15.05 | \n",
" 22.14 | \n",
" ... | \n",
" 0.0 | \n",
" 7.982 | \n",
" 0.468 | \n",
" -0.087 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-09-01 07:52:00+02:00 | \n",
" 26.49 | \n",
" 26.77 | \n",
" 26.77 | \n",
" 1.0 | \n",
" 25.61 | \n",
" 25.51 | \n",
" 23.97 | \n",
" 23.97 | \n",
" 9.40 | \n",
" 21.77 | \n",
" ... | \n",
" 0.0 | \n",
" 2.500 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
2 rows × 38 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" hp_output_MW | \n",
" to_storage_MW | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 41.195801 | \n",
" 40.391060 | \n",
" 42.554082 | \n",
" 24.354573 | \n",
" 0.894037 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 11.278173 | \n",
" 51.226931 | \n",
" 52.003252 | \n",
" 7.151965 | \n",
" 2.633368 | \n",
" 6.814125 | \n",
"
\n",
" \n",
" | min | \n",
" -9.020000 | \n",
" -487.650000 | \n",
" -487.650000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 34.100000 | \n",
" 24.470000 | \n",
" 25.530000 | \n",
" 22.760000 | \n",
" 0.000000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 39.700000 | \n",
" 31.110000 | \n",
" 32.160000 | \n",
" 26.980000 | \n",
" 0.000000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 47.392500 | \n",
" 40.900000 | \n",
" 42.540000 | \n",
" 28.010000 | \n",
" 0.000000 | \n",
" 27.690000 | \n",
"
\n",
" \n",
" | max | \n",
" 121.460000 | \n",
" 936.120000 | \n",
" 936.120000 | \n",
" 31.000000 | \n",
" 25.000000 | \n",
" 30.900000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" hp_output_MW | \n",
" to_storage_MW | \n",
" from_storage_MW | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-01-01 00:00:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:01:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:02:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:03:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:04:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" Total demand | \n",
" hp_input_MW | \n",
" hp_output_MW | \n",
" cop | \n",
" to_storage_MW | \n",
" from_storage_MW | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-11-19 16:28:00+01:00 | \n",
" 70.43 | \n",
" 32.84 | \n",
" 32.84 | \n",
" 27.708 | \n",
" -5.367 | \n",
" 31.000 | \n",
" 5.776 | \n",
" 3.292 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-11-23 06:14:00+01:00 | \n",
" 32.39 | \n",
" 9.38 | \n",
" 60.91 | \n",
" 22.776 | \n",
" -4.138 | \n",
" 22.776 | \n",
" 5.504 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-03 10:27:00+02:00 | \n",
" 33.68 | \n",
" 44.02 | \n",
" 44.02 | \n",
" 22.840 | \n",
" -4.183 | \n",
" 22.840 | \n",
" 5.460 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-10-17 12:26:00+02:00 | \n",
" 48.70 | \n",
" 18.38 | \n",
" 18.38 | \n",
" 19.976 | \n",
" -3.401 | \n",
" 19.976 | \n",
" 5.873 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-21 03:21:00+02:00 | \n",
" 31.49 | \n",
" 39.67 | \n",
" 39.67 | \n",
" 22.703 | \n",
" -3.932 | \n",
" 22.703 | \n",
" 5.773 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-20 07:26:00+01:00 | \n",
" 45.56 | \n",
" 25.03 | \n",
" 25.03 | \n",
" 20.782 | \n",
" -3.550 | \n",
" 20.782 | \n",
" 5.855 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-23 04:28:00+01:00 | \n",
" 33.05 | \n",
" 36.32 | \n",
" 36.32 | \n",
" 26.318 | \n",
" -5.054 | \n",
" 26.318 | \n",
" 5.208 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-30 06:17:00+02:00 | \n",
" 12.27 | \n",
" 20.34 | \n",
" 31.08 | \n",
" 27.509 | \n",
" -5.273 | \n",
" 27.509 | \n",
" 5.217 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-15 04:09:00+02:00 | \n",
" 34.38 | \n",
" 21.86 | \n",
" 21.86 | \n",
" 27.839 | \n",
" -5.169 | \n",
" 27.839 | \n",
" 5.386 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-26 01:38:00+02:00 | \n",
" 33.96 | \n",
" 26.28 | \n",
" 33.56 | \n",
" 20.576 | \n",
" -3.763 | \n",
" 20.576 | \n",
" 5.469 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-10-25 15:40:00+02:00 | \n",
" 35.33 | \n",
" 25.31 | \n",
" 25.31 | \n",
" 29.610 | \n",
" -5.394 | \n",
" 29.610 | \n",
" 5.489 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-18 22:57:00+01:00 | \n",
" 41.70 | \n",
" 29.78 | \n",
" 29.78 | \n",
" 6.835 | \n",
" -2.526 | \n",
" 6.835 | \n",
" 2.706 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-23 09:19:00+02:00 | \n",
" 49.65 | \n",
" 29.84 | \n",
" 29.84 | \n",
" 27.643 | \n",
" -5.198 | \n",
" 27.643 | \n",
" 5.318 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-12-10 10:30:00+01:00 | \n",
" 51.00 | \n",
" 19.88 | \n",
" 19.88 | \n",
" 28.760 | \n",
" -5.476 | \n",
" 31.000 | \n",
" 5.661 | \n",
" 2.240 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-04-24 02:03:00+02:00 | \n",
" 34.05 | \n",
" 40.52 | \n",
" 40.52 | \n",
" 27.701 | \n",
" -5.131 | \n",
" 27.701 | \n",
" 5.399 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-02 09:43:00+02:00 | \n",
" 43.30 | \n",
" 27.78 | \n",
" 27.78 | \n",
" 27.705 | \n",
" -5.262 | \n",
" 27.705 | \n",
" 5.265 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-02-16 08:27:00+01:00 | \n",
" 50.34 | \n",
" 350.34 | \n",
" 350.34 | \n",
" 28.265 | \n",
" -5.053 | \n",
" 28.265 | \n",
" 5.594 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-15 12:18:00+02:00 | \n",
" 38.77 | \n",
" 26.38 | \n",
" 26.38 | \n",
" 27.426 | \n",
" -5.126 | \n",
" 27.426 | \n",
" 5.351 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-11-21 01:17:00+01:00 | \n",
" 35.00 | \n",
" 107.74 | \n",
" 107.74 | \n",
" 28.165 | \n",
" -5.002 | \n",
" 28.165 | \n",
" 5.631 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-01 16:50:00+02:00 | \n",
" 27.60 | \n",
" 23.86 | \n",
" 23.86 | \n",
" 27.630 | \n",
" -5.238 | \n",
" 27.630 | \n",
" 5.275 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" ... | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
" hp_output_MW | \n",
" hp_input_MW | \n",
" cop | \n",
" hp_output_MWh | \n",
" hp_input_MWh | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-01-01 00:00:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 46.83 | \n",
" 52.70 | \n",
" NaN | \n",
" NaN | \n",
" 20.83 | \n",
" 34.24 | \n",
" ... | \n",
" 0.0 | \n",
" 20.510 | \n",
" 147.620 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:01:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 32.12 | \n",
" 54.45 | \n",
" NaN | \n",
" NaN | \n",
" 20.83 | \n",
" 34.24 | \n",
" ... | \n",
" 0.0 | \n",
" 20.387 | \n",
" 147.642 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:02:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 30.97 | \n",
" 48.64 | \n",
" NaN | \n",
" NaN | \n",
" 20.83 | \n",
" 34.24 | \n",
" ... | \n",
" 0.0 | \n",
" 20.264 | \n",
" 147.664 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:03:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 51.13 | \n",
" 48.35 | \n",
" 57.71 | \n",
" 73.05 | \n",
" 20.83 | \n",
" 34.24 | \n",
" ... | \n",
" 0.0 | \n",
" 20.141 | \n",
" 147.686 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:04:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 48.04 | \n",
" 52.01 | \n",
" 63.60 | \n",
" 79.07 | \n",
" 20.83 | \n",
" 34.24 | \n",
" ... | \n",
" 0.0 | \n",
" 20.018 | \n",
" 147.708 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 23 columns
\n",
"